HBASE-21450 [documentation] Point spark doc at hbase-connectors spark

Signed-off-by: Guanghao Zhang <zghao@apache.org>

src/main/asciidoc/_chapters/spark.adoc

@@ -58,7 +58,7 @@ takes in HBase configurations and pushes them to the Spark executors. This allow
 us to have an HBase Connection per Spark Executor in a static location.
 
 For reference, Spark Executors can be on the same nodes as the Region Servers or
-on different nodes there is no dependence of co-location. Think of every Spark
+on different nodes, there is no dependence on co-location. Think of every Spark
 Executor as a multi-threaded client application. This allows any Spark Tasks
 running on the executors to access the shared Connection object.
 
@@ -134,7 +134,7 @@ try {
 All functionality between Spark and HBase will be supported both in Scala and in
 Java, with the exception of SparkSQL which will support any language that is
 supported by Spark. For the remaining of this documentation we will focus on
-Scala examples for now.
+Scala examples.
 
 The examples above illustrate how to do a foreachPartition with a connection. A
 number of other Spark base functions  are supported out of the box:
@@ -148,7 +148,11 @@ access to HBase
 `hBaseRDD`:: To simplify a distributed scan to create a RDD
 // end::spark_base_functions[]
 
-For examples of all these functionalities, see the HBase-Spark Module.
+For examples of all these functionalities, see the
+link:https://github.com/apache/hbase-connectors/tree/master/spark[hbase-spark integration]
+in the link:https://github.com/apache/hbase-connectors[hbase-connectors] repository
+(the hbase-spark connectors live outside hbase core in a related,
+Apache HBase project maintained, associated repo).
 
 == Spark Streaming
 https://spark.apache.org/streaming/[Spark Streaming] is a micro batching stream
@@ -157,12 +161,12 @@ companions in that HBase can help serve the following benefits alongside Spark
 Streaming.
 
 * A place to grab reference data or profile data on the fly
-* A place to store counts or aggregates in a way that supports Spark Streaming
+* A place to store counts or aggregates in a way that supports Spark Streaming's
 promise of _only once processing_.
 
-The HBase-Spark module’s integration points with Spark Streaming are similar to
+The link:https://github.com/apache/hbase-connectors/tree/master/spark[hbase-spark integration]
-its normal Spark integration points, in that the following commands are possible
+with Spark Streaming is similar to its normal Spark integration points, in that the following
-straight off a Spark Streaming DStream.
+commands are possible straight off a Spark Streaming DStream.
 
 include::spark.adoc[tags=spark_base_functions]
 
@@ -202,10 +206,10 @@ dStream.hbaseBulkPut(
 ----
 
 There are three inputs to the `hbaseBulkPut` function.
-. The hbaseContext that carries the configuration boardcast information link us
+The hbaseContext that carries the configuration broadcast information link
-to the HBase Connections in the executors
+to the HBase Connections in the executor, the table name of the table we are
-. The table name of the table we are putting data into
+putting data into, and a function that will convert a record in the DStream
-. A function that will convert a record in the DStream into an HBase Put object.
+into an HBase Put object.
 ====
 
 == Bulk Load
@@ -213,11 +217,11 @@ to the HBase Connections in the executors
 There are two options for bulk loading data into HBase with Spark.  There is the
 basic bulk load functionality that will work for cases where your rows have
 millions of columns and cases where your columns are not consolidated and
-partitions before the on the map side of the Spark bulk load process.
+partitioned before the map side of the Spark bulk load process.
 
-There is also a thin record bulk load option with Spark, this second option is
+There is also a thin record bulk load option with Spark. This second option is
 designed for tables that have less then 10k columns per row.  The advantage
-of this second option is higher throughput and less over all load on the Spark
+of this second option is higher throughput and less over-all load on the Spark
 shuffle operation.
 
 Both implementations work more or less like the MapReduce bulk load process in
@@ -225,7 +229,7 @@ that a partitioner partitions the rowkeys based on region splits and
 the row keys are sent to the reducers in order, so that HFiles can be written
 out directly from the reduce phase.
 
-In Spark terms, the bulk load will be implemented around a the Spark
+In Spark terms, the bulk load will be implemented around a Spark
 `repartitionAndSortWithinPartitions` followed by a Spark `foreachPartition`.
 
 First lets look at an example of using the basic bulk load functionality
@@ -386,20 +390,24 @@ values for this row for all column families.
 
 == SparkSQL/DataFrames
 
-HBase-Spark Connector (in HBase-Spark Module) leverages
+The link:https://github.com/apache/hbase-connectors/tree/master/spark[hbase-spark integration]
+leverages
 link:https://databricks.com/blog/2015/01/09/spark-sql-data-sources-api-unified-data-access-for-the-spark-platform.html[DataSource API]
 (link:https://issues.apache.org/jira/browse/SPARK-3247[SPARK-3247])
-introduced in Spark-1.2.0, bridges the gap between simple HBase KV store and complex
+introduced in Spark-1.2.0, which bridges the gap between simple HBase KV store and complex
 relational SQL queries and enables users to perform complex data analytical work
 on top of HBase using Spark. HBase Dataframe is a standard Spark Dataframe, and is able to
 interact with any other data sources such as Hive, Orc, Parquet, JSON, etc.
-HBase-Spark Connector applies critical techniques such as partition pruning, column pruning,
+The link:https://github.com/apache/hbase-connectors/tree/master/spark[hbase-spark integration]
+applies critical techniques such as partition pruning, column pruning,
 predicate pushdown and data locality.
 
-To use HBase-Spark connector, users need to define the Catalog for the schema mapping
+To use the
+link:https://github.com/apache/hbase-connectors/tree/master/spark[hbase-spark integration]
+connector, users need to define the Catalog for the schema mapping
 between HBase and Spark tables, prepare the data and populate the HBase table,
-then load HBase DataFrame. After that, users can do integrated query and access records
+then load the HBase DataFrame. After that, users can do integrated query and access records
-in HBase table with SQL query. Following illustrates the basic procedure.
+in HBase tables with SQL query. The following illustrates the basic procedure.
 
 === Define catalog
 
@@ -564,8 +572,9 @@ sqlContext.sql("select count(col1) from table").show
 
 .Native Avro support
 ====
-HBase-Spark Connector support different data formats like Avro, Jason, etc. The use case below
+The link:https://github.com/apache/hbase-connectors/tree/master/spark[hbase-spark integration]
-shows how spark supports Avro. User can persist the Avro record into HBase directly. Internally,
+connector supports different data formats like Avro, JSON, etc. The use case below
+shows how spark supports Avro. Users can persist the Avro record into HBase directly. Internally,
 the Avro schema is converted to a native Spark Catalyst data type automatically.
 Note that both key-value parts in an HBase table can be defined in Avro format.
 
@@ -687,4 +696,4 @@ The date frame `df` returned by `withCatalog` function could be used to access t
 After loading df DataFrame, users can query data. registerTempTable registers df DataFrame
 as a temporary table using the table name avrotable. `sqlContext.sql` function allows the
 user to execute SQL queries.
-====
+====